Machine learning has become an integral part of organizations looking to do everything from improve customer experience to make product recommendations to target advertisements. The machine learning (ML) pipeline defines the steps that help create the models used for ML predictions. Each step involved in an ML pipeline is distinct and can be broken into modules to increase reusability for making other ML models. This modularization of ML pipelines creates individual models that become easy to optimize, automate, and integrate with other modules to create an integrated, efficient ML pipeline.
Establishing a well-defined ML pipeline improves productivity for data scientists because the clear steps make automation more straightforward, which leaves little room for human errors and improves the accuracy of models. In addition, a defined ML pipeline ensures every process proceeds efficiently and promotes the reuse of every pipeline stage to build other models.
Let’s explore the ML pipeline architecture and its steps.
What Makes ML Pipeline Architecture Different
The similarities between data pipeline and ML pipeline architecture are many, as they both involve data ingestion, cleaning, and using storage for massive volumes of data. However, an ML pipeline differs from a data analytics pipeline in the following ways:
- Increased complexity from more moving parts and processes: A simple data analytics pipeline involves three key elements: a data source, processing steps, and a destination sink. Creating learning models for a machine learning pipeline introduces additional steps like model training, evaluation, and deployment, which presents complexity to the ML process.
- Data preparation: Both the data preparation steps for analytics and the ML pipeline involve steps like removing/adding values and outliers and changing column names. However, the data preparation steps in an ML pipeline involve additional steps like feature selection, which helps with pattern recognition, regression, classification, and data augmentation to provide richer datasets for creating more innovative models that improve model accuracy and performance. ML pipelines also use data wrangling during the iterative model-building process.
- Tools and technology: The ML process involves convergence, a rigorous iterative process where retraining of models against parameters occurs until models achieve a certain level of accuracy. Hence, an ML pipeline requires scalable, agile, and fast storage methods that can act as model repositories and scale to handle massive datasets resulting from the model training process.
- Monitoring: Models undergo constant monitoring after deployment to evaluate accuracy and performance metrics like latency, efficiency, and throughput. The ML pipeline management system must encourage automation that automatically retrains models on introducing new data to the deployed model to reduce data drift.
Before Designing Your Machine Learning Pipeline Architecture
The ML pipeline represents the application of tools, technologies, and steps to automate the ML process and produce more accurate, efficient models. Every ML pipeline architecture depends on the use case for the models. However, before designing an ML pipeline, here are some essential steps to take:
- Module isolation: As stated earlier, modularization involves separating the ML pipeline into modules to improve the reusability, functionality, and efficiency of pipelines. Each stage of the ML should be well-defined to make it easier to correct, make improvements, and integrate with other modules.
- Map the ML architecture: This mapping accounts for the modules and the static parts of the ML pipeline and creates an architecture that fits into the overall broader organizational structure.
- Orchestrate the ML pipeline: After mapping the ML architecture, the next step is to orchestrate the ML pipeline according to the moving parts, steps, entry points, and module sequence. Employing orchestration tools helps automate this orchestration process and accelerates the ML lifecycle.
- Automate and optimize each stage of the ML pipeline to improve accuracy and efficiency: Modularization helps automate steps in the ML pipeline to be more seamless and optimizes the entire ML lifecycle.
The 5 Steps of Machine Learning Pipeline Architecture
An ML pipeline architecture involves the following steps:
- Data ingestion: This represents the data collection steps for the model and involves data collection from multiple ingestion points like data lakes, warehouses, on-premises or cloud databases, or Internet of Things (IoT) devices. Data ingestion can occur in batches or continuously via streams. Because of the massive volumes of data involved in ingestion, organizations usually employ a separate pipeline for each ingestion point to run independently and parallel to each other to improve performance.
- Data preprocessing: This step involves preparing the ingested data for use in model training. As data arrives, it’s usually unsuitable for use and needs to undergo multiple preprocessing steps like cleaning, transformation and integration before using it for model training. Preprocessing steps include data cleaning, reduction, transformation, and feature engineering to create a rich, high-quality dataset.
- Model training: Prepared data from the previous step undergoes splitting into the testing and validation datasets. The testing set undergoes iterative training with training algorithms to find data patterns until it achieves convergence and tests against the validation dataset. In an automated process, the model training service takes the training configuration details and stores the model, configurations, training parameters, and other elements in a model candidate repository, which can be reevaluated and reused further down the pipeline.
- Model tuning and deployment: This stage involves fine-tuning models against multiple datasets to further improve accuracy before model deployment. In model deployment, the best models are pushed to production and used to make predictions on live data.
- Post-deployment model optimization: After deployment, models undergo continuous monitoring to observe their performance in production by noting metrics like accuracy, data drift, and latency. This post-deployment optimization also utilizes automation to automatically retrain datasets to prevent model drift and ensure the long-term accuracy of models.
Modular, StreamSets, and Your ML Models
A modular approach to building ML pipelines is a best practice for ML operations (MLOps). Modularization in ML treats each stage in the ML pipeline as a module, making it easy to repurpose/reuse, upscale/downscale individual modules as needed, and improve end-to-end pipeline management.
Taking a modular approach means you can pick and choose the best in class technologies for each step in your ML pipeline. A data integration platform like StreamSets can automate and templatize the process of data ingestion without the need to know a lot of code. So, your teams can move from your raw data source to important insights about your business quickly. Furthermore, StreamSets Transformer allows data teams to automate common ML data preprocessing tasks like changing data types, column names, and renaming features. These tools can make the process of creating and maintaining ML pipelines easier by making the building blocks of ML pipelines, data ingestion and data preprocessing more streamlined.